By some estimates, up to half of all mechanical failures in process plants are induced by process conditions. Therefore, providing feedback to an operator that the process machines are being operated in a non-optimal configuration provides a way for the operator to avoid harmful operating states, thereby substantially extending mean time between failures (MTBF) or mean time between repairs (MTBR) on production assets.
Vibration analysis is a well proven technology for detecting faults in rotating machinery. The process of determining the severity and specifics of a fault can be very involved. Part of the analysis process involves determining whether periodic signals are present. While maintenance personnel are concerned with detailed analyses of faults, operations personnel only want to know if a problem exists. Providing a few fault-related parameters to the operator can be sufficient in accomplishing this task. Fault-related parameters can be related to amplitudes of energy from particular vibration frequencies (bandwidth), signal processing techniques such as PeakVue™, and the presence of periodic and non-periodic signals. Parameters calculated from bandwidth and signal processing techniques are well defined. However, a parameter indicating the presence of periodic and non-periodic signals has not been defined.
Further, the ability to detect mechanical faults in industrial rotating equipment is a task requiring skilled analytical personnel with years of training and experience. The technician performing the machine diagnosis must be skilled in the techniques and technologies used to analyze the machine. A typical vibration spectrum used for such analysis will contain 1,600 data points, but may contain upwards of 12,800 points. Practically, only a handful of these data values are significant for the diagnosis of the machine. It typically takes several weeks of training followed by 18-24 months of practice for the technician to be skilled in identifying the handful of peaks that are required for the diagnosis. Developing and maintaining employees who are qualified to serve as technicians is a major concern in industry, because an individual plant may only have one such individual on staff. This dynamic is further exacerbated by the trend towards having a central diagnostician be responsible for analyzing data collected across multiple plant sites—further reducing the availability of redundant skills within the organization. Therefore, new technologies and data plots are required that will reduce training requirements and simplify the identification of pertinent data points within the larger data set.
Additionally, a vibration analyst needs tools to help differentiate between non-periodic and periodic information in a vibration signal. For example, analysis tools are needed to extract a low-amplitude periodic signal (e.g. 10 g signal) indicating a bearing fault out of a large non-periodic signal (e.g. 70 g signal) caused by under lubrication. This is a common situation, in which a lack of adequate lubrication inevitably leads to an actual mechanical defect in the bearing. Catching it early is very important to extended machine life.
A separate but equally concerning dynamic is that a single individual is being asked to analyze the data from multiple sites. In such situations, even an experienced analyst requires additional tools that pre-select and extract pertinent information from the larger data set, thereby significantly reducing the amount of data that must be screened by the analyst, streamlining the diagnostic process, and increasing both the efficiency and accuracy of the diagnosis.
Further, the management of large data sets presents a continual challenge for any individual required to interface with the data. This includes transmission, storage and retrieval of collected data.                Transmission. Due to large data sets required for traditional vibration analysis, transmission of vibration data can be very challenging with smaller or restricted data pipes. One example is transmission via a wireless link, where specific bandwidth allocations exist. Another example is the application of a prescribed wireless protocol, such as HART® or WirelessHART®, where each data packet has a pre-defined size that is far too small to accommodate a traditional vibration measurement.        Storage. Drastic reductions in the cost of computer memory have lead many disciplines, including vibration analysis, to develop tools that are extensively data centric. However, with the advent of cloud-based data storage, the cost of memory is increasing again, forcing users to prioritize which information is stored or retained.        Retrieval. Relational databases developed to support business systems have proven to be less than ideal for vibration analysis. This is due to the large file size of vibration readings. As the size of a relational database grows, the retrieval time to access and display a specific data set increases significantly. Given that a typical vibration spectrum may be composed of over 12,800 data points, and the raw vibration waveform associated with this spectrum may have over 30,000 data points, and that there may be tens of thousands of such measurements in a typical process facility, it is easy to see how the size of a vibration database could quickly become inappropriate for the application of a relational database.        
For these reasons, there is a critical need to develop new techniques to reduce training requirements, improve the efficiency of an analyst without compromising accuracy, enable data transmission across limited data pipes, reduce vibration traffic across larger data pipes, reduce memory requirements to store diagnostic data, and enable users to access and display stored data with high responsiveness and faster retrieval times.